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  1. Article ; Online: Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors.

    Wang, Qian / Dong, Wen / Yang, Kun / Ren, Zhongda / Huang, Dongqing / Zhang, Peng / Wang, Jie

    International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

    2021  Volume 105, Page(s) 675–685

    Abstract: ... of COVID-19 transmission and its influencing factors in China, from January to October 2020.: Methods ... the relationship between influencing factors and confirmed COVID-19 cases.: Results: The distribution of COVID ... 19 in China tends to be stable over time, with spatial correlation and prominent clustering regions ...

    Abstract Objectives: The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China, from January to October 2020.
    Methods: About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases.
    Results: The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and prominent clustering regions. Spatio-temporal scanning analysis showed that most COVID-19 high-incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR=491.57) which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases.
    Conclusions: Environmental temperature has a certain inhibitory effect on the transmission of COVID-19; the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.
    MeSH term(s) Air Pollution ; COVID-19/transmission ; China/epidemiology ; Humans ; Risk Factors ; SARS-CoV-2 ; Spatio-Temporal Analysis ; Temperature
    Language English
    Publishing date 2021-03-09
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 1331197-9
    ISSN 1878-3511 ; 1201-9712
    ISSN (online) 1878-3511
    ISSN 1201-9712
    DOI 10.1016/j.ijid.2021.03.014
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.

    Chen, Youliang / Li, Qun / Karimian, Hamed / Chen, Xunjun / Li, Xiaoming

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 3717

    Abstract: ... are several studies that investigating the influencing factors on distribution of COVID-19 in China ... the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks ... In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding ...

    Abstract In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China's non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = - 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.
    MeSH term(s) COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/transmission ; China ; Humans ; Prevalence ; Quarantine/statistics & numerical data ; Rural Population/statistics & numerical data ; Spatio-Temporal Analysis ; Urban Population/statistics & numerical data ; Weather
    Language English
    Publishing date 2021-02-12
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-83166-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China-An Analysis Based on 417 Cases.

    Liu, Shirui / Qin, Yaochen / Xie, Zhixiang / Zhang, Jingfei

    International journal of environmental research and public health

    2020  Volume 17, Issue 20

    Abstract: ... to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were ... Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have ... of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China ...

    Abstract The global pandemic of COVID-19 has made it the focus of current attention. At present, the law of COVID-19 spread in cities is not clear. Cities have long been difficult areas for epidemic prevention and control because of the high population density, high mobility of people, and high frequency of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China. The nearest neighbor index method, kernel density method, and the standard deviation ellipse method were used to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were then explored using the multiple linear regression method. The results show that: (1) The development of COVID-19 epidemic situation in Shenzhen occurred in three stages. The patients showed significant hysteresis from the onset of symptoms to hospitalization and then to diagnosis. Prior to 27 January, there was a relatively long time interval between the onset of symptoms and hospitalization for COVID-19; the interval decreased thereafter. (2) The epidemic site (the place where the patient stays during the onset of the disease) showed an agglomeration in space. The degree of agglomeration constantly increased across the three time nodes of 31 January, 14 February, and 22 February. The epidemic sites formed a "core area" in terms of spatial distribution and spread along the "northwest-southeast" direction of the city. (3) Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have not played a significant role.
    MeSH term(s) COVID-19 ; China/epidemiology ; Cities/epidemiology ; Coronavirus Infections/epidemiology ; Coronavirus Infections/therapy ; Coronavirus Infections/transmission ; Humans ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/therapy ; Pneumonia, Viral/transmission ; Risk Factors ; Socioeconomic Factors ; Spatio-Temporal Analysis
    Keywords covid19
    Language English
    Publishing date 2020-10-13
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph17207450
    Database MEDical Literature Analysis and Retrieval System OnLINE

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